Artificial Intelligence Comes to Hollywood

Last September, when the 20th Century Fox sci-fi thriller Morgan premiered, artificial intelligence (AI) took center stage for the first time not as a plot point but a tool. The film studio revealed that it had used IBM’s Watson — a supercomputer endowed with AI capabilities — to make the movie’s trailer. IBM research scientists “taught” Watson about horror movie trailers by feeding it 100 such trailers, cut into scenes. Watson then analyzed the data, from the point of view of visuals, audio and emotions, to “learn” what makes a horror trailer scary. Then the scientists fed in the entire 90-minute Morgan. According to Engadget, Watson “instantly zeroed in on 10 scenes totaling six minutes of footage.”

The media buzz that followed both overstated and understated what had actually happened. In fact, an actual human being edited the trailer, using the scenes Watson chose. So AI didn’t actually edit the trailer. But it was also a benchmark, tantalizing the Hollywood creatives (and studio executives) interested in how artificial intelligence might change entertainment.

Philip Hodgetts

The discussion about AI is still a bit premature; when today’s products are described, machine learning is a more accurate description. The first person to posit that machines could actually learn was computer gaming pioneer Arthur Samuels, in 1959. Based on pattern recognition and dependent on enough data to train the computer, machine learning is used for any repetitive task. Philip Hodgetts, who founded two companies integrating machine learning, Intelligent Assistance and Lumberjack System, notes that “there’s a big leap from doing a task really well to a generalized intelligence that can do multiple self-directed tasks.” Most experts agree that autonomous cars are the closest we have today to a real-world artificial intelligence.

The Buzz and the Reality

Machine learning can and does form an important role in a growing number of applications aimed at the media and entertainment business, nearly all of them invisible to the end user. Perhaps the most obvious ones are the applications aimed at distribution of digital media. Iris.TV, which partners with numerous media companies from Time Warner’s Telepictures Productions to Hearst Digital Media, uses machine learning to create what it dubs “personalized video programming.” The company takes in the target company’s digital assets and creates a taxonomy and structure, with the metadata forming the basis of recommendations. The APIs, which integrate with most video players, learn what the user watches, then create a playlist based on those preferences. The results are pretty impressive: The Hollywood Reporter, for example, was able to double its video views from 80 million in October 2016 to 210 million in February 2017.

Machine learning also plays an increasingly significant role in video post-production — much more so than production, which is still a hands-on, very human job. “The production process is dependent on bipedal mobility,” notes Hodgetts wryly. “We’ve motorized cranes and so on, but it’ll be harder to replace a runner on set.” Even so, the process of creating digital imagery will feel the impact of machine learning in the not-so-distant future. Adobe, for example, is working with the Beckman Institute for Advanced Science and Technology to use a kind of machine learning to teach a software algorithm how to distinguish and eliminate backgrounds. With the goal of automating compositing, the software has been taught to do so via a dataset of 49,300 training images.

Tools in the Marketplace Today

Today’s machine learning-enhanced tools fall under the umbrella of cognitive services, a term that covers any off-the-shelf programs that have already been trained at a task, whether it’s facial recognition or motion detection. At NAB 2017, Finnish company Valossa will debut its Alexa-integrated real-time video recognition platform, Val.ai.

Val.ai is intended to solve the problem of discoverability. “Companies that have lots of media assets and want to monetize them better fall into this category,” says Valossa chief executive founder Mika Rautiainen. “Or they can also re-use archived material for new content. Increasingly, we’ve found other scenarios emerging in the years we’ve been creating the service related to content analytics. Deep content understanding correlated with user behavior lets media companies serve contextual advertising and other end-user experiences around media.” The Valossa video intelligence engine is in beta at 120 companies, the majority of which are in the U.S. and the U.K.

Rautiainen states that content analytics can also be used to promote and sell items in a video, a capability that Valossa is not developing. “But I was surprised how many companies are working around reinventing retail or the purchasing process,” he says. Valossa also has a technology demo for facial-expression recognition, which Rautiainen calls a “next-level intelligence,” and Valossa Movie Finder, with a database of metadata from 140,000 movies.

Yvonne Thomas

Arvato Systems will debut its next-generation MAM system, Media Portal, at NAB 2017. Yvonne Thomas, the company’s product manager for the broadcast solutions division, says Media Portal “integrates analytics and machine learning via an API, and indexes/updates the respective media. It will also support the visualization for the user in the form of facets that can handle a wide range of data.”

At Piksel, chief technology officer Mark Christie points out that “machine learning capabilities have accelerated dramatically in recent years and, through natural-language processing techniques, they can now enable a deeper understanding of content.” In 2016, Piksel acquired Lingospot, with its “patented and patent-pending natural language processing, semantic search, image analysis and machine-learning technologies,” and integrated it into Piksel’s Palette, to collect proprietary metadata on a scene-by-scene basis. Its Fuse, which is built on Piksel Palette, enriches metadata with cast and crew lists or other documentation from third-party sources and serves it across broadcast and OTT workflows.

But Will I Lose My Job?

Although the advent of tools enhanced by machine learning is interesting, most people in the entertainment industry want to know how worried they should be about their jobs. Hodgetts has a simple answer. “If you can teach someone your job in three days, it will be automated [via machine learning],” he says.

At USC School of Cinematic Arts, professor and editor Norman Hollyn has been thinking about the implications of collecting metadata for a long time. In principle, automation of what used to be a tedious, labor-intensive job could wreak major changes on the job of the assistant editor. Hollyn has a more positive spin on the integration of these new tools.

“About three years ago, I started realizing the value of machine learning and artificial intelligence,” he says. “With my background, I knew just how difficult it was for humans to collect data, and I started thinking about how much easier my work would be if database fields could be automatically filled.”

He agrees that machine learning will change the job of the assistant editor. “Historically, even back in the 35mm days, the assistant editor was really an incredibly specialized librarian,” he says. “It’s not a huge difference today. But once machine learning takes over, the librarian work will easily be taken over.”

But the results, he thinks, won’t be all bad. On some productions, he believes, that there will be no assistants. On others, assistants may be involved in such tasks as “world-building” for cross-platform media or cutting trailers. “When I think about what my students may be doing in five years, it’s bad news if they think they want to be assistant editors on a TV job,” he says. “But they can play a role in building the world out of which comes movies, TV series, games, VR and comic books. Different people have to organize that world-building — and that’s not a machine-learning capability yet.”

The post-production environment always feels the downward budgetary pressure and probably offers less flexibility for facility owners trying to keep afloat. “AI will be good and bad for people in our industry,” says AlphaDogs Chief Executive Terence Curren. “The level of AI we currently have can already automate many tasks that used to employ people. Automated syncing and grouping of clips is just one example. As AI gets smarter, more jobs will be replaced, but the removal of the human element will also eliminate many mistakes that currently cost time down the pipeline. The bottom line is, if you do something that is repetitive all day, your job will be one of the first to get replaced. If you do something creative, that requires constantly changing approaches, your job will be safe for a long time.”

For those worried about the ethical considerations of bringing machine learning and artificial intelligence into the workplace (as well as potentially hundreds of consumer-facing products and services), that’s being addressed both by giant technology companies and the IEEE. In September 2016, Google, Facebook, Amazon, IBM and Microsoft formed the Partnership on Artificial Intelligence to Benefit People and Society, to advance public understanding of the technologies and come up with standards. The Partnership says it plans to “conduct research, recommend best practices and publish research under an open license in areas such as ethics, fairness and inclusivity; transparency, privacy and interoperability; collaboration between people and AI systems; and the trustworthiness, reliability and robustness of the technology.” Apple just joined the group.

Meanwhile, the IEEE and its Standards Association created a new standards project, IEEE P700, a working group that “intends to define a process model by which engineers and technologists can address ethical considerations throughout the various stages of system initiation, analysis and design” for big data, machine learning and artificial intelligence.”

Machine learning is here, and AI is coming, not just to the entertainment industry but many others. There will be winners and losers, but the very human talent of creativity — a specialty in the entertainment industry — is safe for the foreseeable future.

About the Author

Debra Kaufman, a longtime entertainment and media industry writer, stays abreast of machine learning and artificial intelligence as a writer for the newsletter of USC think tank Entertainment Technology Center.

Did you enjoy this article? Sign up to receive the StudioDaily Fix eletter containing the latest stories, including news, videos, interviews, reviews and more.